Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||5|
|Journal||IEEE Signal Processing Letters|
|Publication status||Published - 1 Aug 2018|
|Publication type||A1 Journal article-refereed|
We introduce a paradigm for nonlocal sparsity reinforced deep convolutional neural network denoising. It is a combination of a local multiscale denoising by a convolutional neural network (CNN) based denoiser and a nonlocal denoising based on a nonlocal filter (NLF), exploiting the mutual similarities between groups of patches. CNN models are leveraged with noise levels that progressively decrease at every iteration of our framework, while their output is regularized by a nonlocal prior implicit within the NLF. Unlike complicated neural networks that embed the nonlocality prior within the layers of the network, our framework is modular, and it uses standard pretrained CNNs together with standard nonlocal filters. An instance of the proposed framework, called NN3D, is evaluated over large grayscale image datasets showing state-of-the-art performance.
- BM3D, convolutional neural network, image denoising, nonlocal filters